Architecting Big Data Applications: Batch Mode Application Engineering

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Kumaran Ponnambalam

1:28:14

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01 - Introduction

01 - Architecting big data applications.mp4

00:41

02 - 1. Introduction to Big Data Applications

01 - Characteristics of batch processing.mp4

01:53

02 - Challenges building batch applications.mp4

01:15

03 - Technologies for batch big data engineering.mp4

02:01

04 - Use cases for batch big data.mp4

02:19

05 - Architecture process for data engineering.mp4

02:15

03 - 2. Big Data Architecture Principles

01 - Making the choice Real-time vs. batch.mp4

03:37

02 - Horizontal scaling.mp4

03:53

03 - Distributed processing.mp4

02:01

04 - Technology selection.mp4

04:16

05 - Technology integrations.mp4

02:03

04 - 3. Batch Application Architecture Principles

01 - Schedule selection.mp4

02:40

02 - Minimizing data volumes.mp4

02:48

03 - Uniform load distribution.mp4

03:51

04 - Using caches.mp4

02:04

05 - Reprocessing.mp4

02:14

05 - 4. Use Case 1 Audit Trail Data Archive

01 - Audit trail Define the problem.mp4

02:39

02 - Audit trail Study requirements.mp4

03:29

03 - Audit trail Create a workflow.mp4

01:54

04 - Audit trail Scale the workflow.mp4

02:24

05 - Audit trail Select technologies.mp4

04:26

06 - Audit trail Review final architecture.mp4

01:58

06 - 5. Use Case 2 Advertising Analytics

01 - Advertising analytics Define the problem.mp4

02:34

02 - Advertising analytics Study requirements.mp4

03:04

03 - Advertising analytics Create a workflow.mp4

01:53

04 - Advertising analytics Scale the workflow.mp4

02:45

05 - Advertising analytics Select technologies.mp4

04:38

06 - Advertising analytics Review final architecture.mp4

01:21

07 - 6. Use Case 3 Product Recommendations

01 - Product recommendations Define the problem.mp4

01:58

02 - Product recommendations Study requirements.mp4

03:02

03 - Product recommendations Create a workflow.mp4

01:24

04 - Product recommendations Scale the workflow.mp4

02:48

05 - Product recommendations Select technologies.mp4

03:59

06 - Product recommendations Review the final architecture.mp4

01:24

08 - Conclusion

01 - Continuing to architect big data applications.mp4

00:43

Description

Big data applications allow data scientists and analysts to acquire, store, manage, and use big data to generate more consistent, data-driven results. In this course, instructor Kumaran Ponnambalam explores real-world business use cases and best practices for architecting big data applications using existing open-source technologies.

Learn how to architect both simple and complex batch processing applications, as you discover the basic principles of big data architectures such as horizontal scaling, distributed processing, technology selection and integration, and scheduling. Kumaran shows you how to minimize data volumes and distribute data loads uniformly, as well as how to use caches, reprocess data, troubleshoot errors, and more. Along the way, take your new skills to the next level with hands-on use cases that cover a variety of functional and technology domains.

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Big Data

Kumaran Ponnambalam

Instructor's Courses

A seasoned veteran in everything data, with a reputation for delivering high performance database and SaaS applications and currently specializing in leading Big Data Science and Engineering efforts

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